Skip to content

Allow Y to be a tensor #180

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Merged
merged 4 commits into from
Aug 19, 2024
Merged

Allow Y to be a tensor #180

Changes from all commits
Commits
Show all changes
4 commits
Select commit Hold shift + click to select a range
4dfb08a
Allow Y to be a tensor
aloctavodia Aug 19, 2024
cce5c57
[pre-commit.ci] auto fixes from pre-commit.com hooks
pre-commit-ci[bot] Aug 19, 2024
1f98c53
Update pgbart.py
aloctavodia Aug 19, 2024
09d5528
[pre-commit.ci] auto fixes from pre-commit.com hooks
pre-commit-ci[bot] Aug 19, 2024
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
13 changes: 9 additions & 4 deletions pymc_bart/pgbart.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -138,6 +138,11 @@ def __init__( # noqa: PLR0915
else:
self.X = self.bart.X

if isinstance(self.bart.Y, Variable):
self.Y = self.bart.Y.eval()
else:
self.Y = self.bart.Y

self.missing_data = np.any(np.isnan(self.X))
self.m = self.bart.m
self.response = self.bart.response
Expand Down Expand Up @@ -166,26 +171,26 @@ def __init__( # noqa: PLR0915
if rule is ContinuousSplitRule:
self.X[:, idx] = jitter_duplicated(self.X[:, idx], np.nanstd(self.X[:, idx]))

init_mean = self.bart.Y.mean()
init_mean = self.Y.mean()
self.num_observations = self.X.shape[0]
self.num_variates = self.X.shape[1]
self.available_predictors = list(range(self.num_variates))

# if data is binary
self.leaf_sd = np.ones((self.trees_shape, self.leaves_shape))

y_unique = np.unique(self.bart.Y)
y_unique = np.unique(self.Y)
if y_unique.size == 2 and np.all(y_unique == [0, 1]):
self.leaf_sd *= 3 / self.m**0.5
else:
self.leaf_sd *= self.bart.Y.std() / self.m**0.5
self.leaf_sd *= self.Y.std() / self.m**0.5

self.running_sd = [
RunningSd((self.leaves_shape, self.num_observations)) for _ in range(self.trees_shape)
]

self.sum_trees = np.full(
(self.trees_shape, self.leaves_shape, self.bart.Y.shape[0]), init_mean
(self.trees_shape, self.leaves_shape, self.Y.shape[0]), init_mean
).astype(config.floatX)
self.sum_trees_noi = self.sum_trees - init_mean
self.a_tree = Tree.new_tree(
Expand Down
Loading